Zhongjin Li

A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds

Security is increasingly critical for various scientific workflows that are big data applications and typically take quite amount of time being executed on large-scale distributed infrastructures. Cloud computing platform is such an infrastructure that can enable dynamic resource scaling on demand. Nevertheless, based on pay-per-use and hourly-based pricing model, users should pay attention to the cost incurred by renting virtual machines (VMs) from cloud data centers. Meanwhile, workflow tasks are generally heterogeneous and require different instance series (i.e., computing optimized, memory optimized, storage optimized, etc.). In this paper, we propose a security and cost aware scheduling (SCAS) algorithm for heterogeneous tasks of scientific workflow in clouds. Our proposed algorithm is based on the meta-heuristic optimization technique, particle swarm optimization (PSO), the coding strategy of which is devised to minimize the total workflow execution cost while meeting the deadline and risk rate constraints. Extensive experiments using three real-world scientific workflow applications, as well as CloudSim simulation framework, demonstrate the effectiveness and practicality of our algorithm. This algorithm allows users to trade off the security and cost of workflow.The heterogeneous tasks are taken into account in workflow scheduling.This algorithm based on PSO considers heterogeneous VM resources.A coding strategy is devised to solve the multi-constraint optimization problem.

Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds

Cloud computing is a suitable platform to execute the deadline-constrained scientific workflows which are typical big data applications and often require many hours to finish. Moreover, the problem of energy consumption has become one of the major concerns in clouds. In this paper, we present a cost and energy aware scheduling (CEAS) algorithm for cloud scheduler to minimize the execution cost of workflow and reduce the energy consumption while meeting the deadline constraint. The CEAS algorithm consists of five sub-algorithms. First, we use the VM selection algorithm which applies the concept of cost utility to map tasks to their optimal virtual machine (VM) types by the sub-makespan constraint. Then, two tasks merging methods are employed to reduce execution cost and energy consumption of workflow. Further, In order to reuse the idle VM instances which have been leased, the VM reuse policy is also proposed. Finally, the scheme of slack time reclamation is utilized to save energy of leased VM instances. According to the time complexity analysis, we conclude that the time complexity of each sub-algorithm is polynomial. The CEAS algorithm is evaluated using Cloudsim and four real-world scientific workflow applications, which demonstrates that it outperforms the related well-known approaches.

Energy cost minimization with job security guarantee in Internet data center

With the proliferation of various big data applications and resource demand from Internet data centers (IDCs), the energy cost has been skyrocketing, and it attracts a great deal of attention and brings many energy optimization management issues. However, the security problem for a wide range of applications, which has been overlooked, is another critical concern and even ranked as the greatest challenge in IDC. In this paper, we propose an energy cost minimization (ECM) algorithm with job security guarantee for IDC in deregulated electricity markets. Randomly arriving jobs are routed to a FIFO queue, and a heuristic algorithm is devised to select security levels for guaranteeing job risk probability constraint. Then, the energy optimization problem is formulated by taking the temporal diversity of electricity price into account. Finally, an online energy cost minimization algorithm is designed to solve the problem by Lyapunov optimization framework which offers provable energy cost optimization and delay guarantee. This algorithm can aggressively and adaptively seize the timing of low electricity price to process workloads and defer delay-tolerant workloads execution when the price is high. Based on the real-life electricity price, simulation results prove the feasibility and effectiveness of proposed algorithm. The energy cost optimization architecture is proposed for IDC operator.A heuristic algorithm is devised to select security services to guarantee the job security.The temporal diversity of electricity price is considered in minimizing the energy cost.The energy cost minimization algorithm is based on Lyapunov optimization technique.Extensive evaluation experiments demonstrate the effectiveness of our algorithms.

Task Offloading for Scientific Workflow Application in Mobile Cloud.

Scientific applications are typically data-intensive, which feature complex DAG-structured workflows comprised of tasks with intricate inter-task dependencies. Mobile cloud computing (MCC) provides significant opportunities in enhancing computation capability and saving energy of smart mobile devices (SMDs) by offloading computation-intensive and data-intensive tasks from resource limited SMDs onto the resource-rich cloud. However, finding a proper way to assist SMDs in executing such applications remains a crucial concern. In this paper, we offer three entry points for the problem solving: first, a cost model based on the pay-as-you-go manner of IaaS Cloud is proposed; then, we investigate the problem of mapping strategy of scientific workflows to minimize the monetary cost and energy consumption of SMDs simultaneously under deadline constraints; furthermore, we consider dataset placement issue during the offloading and mapping process of the workflows. A genetic algorithm (GA) based offloading method is proposed by carefully modifying parts of GA to suit the needs for the stated problem. Numerical results corroborate that the proposed algorithm can achieve near-optimal energy and monetary cost reduction with the application completion time and dataset placement constraint satisfied.

A load-aware resource allocation and task scheduling for the emerging cloudlet system

Abstract Cloudlet-assisted mobile cloud computing (MCC) emerges as a vital paradigm to address the problems of mobile services such as application time-out, data caching and traffic congestion in wireless network. The cloudlet has adequate resources to process multiple mobile requests simultaneously, but it is not as sufficient as a remote cloud data center. Currently the performance of MCC system is a subject to the lengthy network transmission latency due to the long distance between cloudlet and remote cloud. In this article, we focus on the variable user’s QoS requirements and budget of cloudlet provider, design a load-aware resource allocation and task scheduling (LA-RATS) strategy which adaptively allocates resource in MCC system for delay-tolerant and delay-sensitive mobile applications according to cloudlet’s load profile. Subsequently, a tree generation based task backfilling algorithm is proposed to raise the utilization of the cloudlet. Particularly, when cloudlet is overloaded, the restrictions of delay-sensitive applications’ deadlines are satisfied through further offloading the allocated delay-tolerant tasks in the cloudlet to distant cloud. From several systematic evaluations, it is shown that our strategy can significantly reduce the cloudlet’s monetary cost and turnaround time for delay-tolerant applications, and increase the deadline satisfaction rate of delay-sensitive applications.

Automatically Classify Chinese Judgment Documents Utilizing Machine Learning Algorithms

In law, a judgment is a decision by a court that resolves a controversy and determines the rights and liabilities of parties in a legal action or proceeding. In 2013, China Judgments Online system was launched officially for record keeping and notification, up to now, over 23 million electronic judgment documents are recorded. The huge amount of judgment documents has witnessed the improvement of judicial justice and openness. Document categorization becomes increasingly important for judgments indexing and further analysis. However, it is almost impossible to categorize them manually due to their large volume and rapid growth. In this paper, we propose a machine learning approach to automatically classify Chinese judgment documents using machine learning algorithms including Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and Support Vector Machine (SVM). A judgment document is represented as vector space model (VSM) using TF-IDF after words segmentation. To improve performance, we construct a set of judicial stop words. Besides, as TF-IDF generates a high dimensional feature vector, which leads to an extremely high time complexity, we utilize three dimensional reduction methods. Based on 6735 pieces of judgment documents, extensive experiments demonstrate the effectiveness and high classification performance of our proposed method.

Energy Cost Minimization with Risk Rate Constraint for Internet Data Center in Deregulated Electricity Markets

With the large-scale development of internet data center (IDC), the energy cost is increasing significantly and has attracted a great deal of attention. Moreover, existing scheduling optimization methods for cloud computing applications disregard the security services. In this paper, we propose a long-term energy cost minimization (ECM) algorithm with risk rate constraint for an internet data center in deregulated electricity markets. First, we formulate the stochastic optimization problem taking the temporal diversity of electricity price and risk rate constraint into account. Then, an operation algorithm is designed to solve the problem by Lyapunov optimization framework, which offers provable energy cost and delay guarantees. Extensive evaluation experiments based on the real-life electricity price demonstrate the effectiveness of proposed algorithm.

Fault-Tolerant Scheduling for Scientific Workflow with Task Replication Method in Cloud.

Cloud computing has become a revolutionary paradigm by provisioning on-demand and low cost computing resources for customers. As a result, scientific workflow, which is the big data application, is increasingly prone to adopt cloud computing resources. However, internal failure (host fault) is inevitable in such large distributed computing environment. It is also well studied that cloud data center will experience malicious attacks frequently. Hence, external failure (failure by malicious attack) should also be considered when executing scientific workflows in cloud. In this paper, a fault-tolerant scheduling (FTS) algorithm is proposed for scientific workflow in cloud computing environment, the aim of which is to minimize the workflow cost with the deadline constraint even in the presence of internal and external failures. The FTS algorithm, based on tasks replication method, is one of the widely used fault tolerant mechanisms. The experimental results in terms of real-world scientific workflow applications demonstrate the effectiveness and practicality of our proposed algorithm.

Automatically Classifying Chinese Judgment Documents Using Character-Level Convolutional Neural Networks.

Judgment is a decision by a court or other tribunal that resolves a controversy and determines the rights and obligations of the parties. Since the establishment of the China Judgments Online System, more and more judgment documents have been stored online. With the explosive growth of the number of Chinese judgment documents, the need for automated classification methods is getting increasingly urgent. For Chinese data sets, traditional word-level methods often bring extra errors in word segmentation. In this paper, we proposed an approach based on character-level convolutional neural networks to automatically classify Chinese judgment documents. Different from traditional machine learning methods, we hand over the work of feature detection to the model. Throughout the process, the only part that requires human labor is labeling the category of each original documents. In order to prevent overfitting when the amount of training data is not very large, we use a shallow model which has only one convolution layer. The proposed approach does well in achieving high classification accuracy based on 7923 pieces of Chinese judgment documents. In the meanwhile, the effectiveness of our model is satisfactory.

Multi-objective scheduling for scientific workflow in multicloud environment

Abstract Providing resources and services from multiple clouds is becoming an increasingly promising paradigm. Workflow applications are becoming increasingly computation-intensive or data-intensive, with its resource requirement being maintained from multicloud environment in terms of pay-per-use pricing mechanism. Existing works of cloud workflow scheduling primarily target optimizing makespan or cost. However, the reliability of workflow scheduling is also a critical concern and even the most important metric of QoS (quality of service). In this paper, a multi-objective scheduling (MOS) algorithm for scientific workflow in multicloud environment is proposed, the aim of which is to minimize workflow makespan and cost simultaneously while satisfying the reliability constraint. The proposed MOS algorithm is according to particle swarm optimization (PSO) technology, and the corresponding coding strategy takes both the tasks execution location and tasks order of data transmission into consideration. On the basis of real-world scientific workflow models, extensive simulation experiments demonstrate the significant multi-objective performances improvement of MOS algorithm over the CMOHEFT algorithm and the RANDOM algorithm.